In digital radiography applications, non-functioning pixels of the digital detector, and particularly groups of non-functioning pixels, can create areas of anomalous gray value that appear brighter or darker than surrounding pixels on the displayed images and serve to distract the user. Therefore, it is important to be able to identify any non-functioning pixels and correct the corresponding gray values in the displayed images.
Performing such identification and correction is particularly difficult in real-time digital radiography applications. Processing large-format digital detector data to produce images in real time at 30 million pixels per second is computationally intensive. Accordingly, the identification of non-functioning pixels, and the subsequent correction of the anomalous digital data they produce, must be done with efficient usage of limited processor and memory resources. Current methods for performing non-functioning pixel correction result in significant processing times that might limit the speed of the system, and/or result in reduced image quality due to poor identification and correction algorithms. There is a need for such identification and correction to be done more quickly than in existing systems. In addition, there is a need to be able to more accurately identify pixels as being non-functioning.